De Moor B - Search Results

1

Optimal control by least squares support vector machines.

Suykens JA, Vandewalle J, De Moor B. Suykens JA, et al. Among authors: de moor b. Neural Netw. 2001 Jan;14(1):23-35. doi: 10.1016/s0893-6080(00)00077-0. Neural Netw. 2001. PMID: 11213211

2

NL(q) Theory: A Neural Control Framework with Global Asymptotic Stability Criteria.

Vandewalle J, De Moor BL, Suykens JA. Vandewalle J, et al. Neural Netw. 1997 Jun;10(4):615-637. doi: 10.1016/s0893-6080(96)00104-9. Neural Netw. 1997. PMID: 12662859

3

Approximate confidence and prediction intervals for least squares support vector regression.

De Brabanter K, De Brabanter J, Suykens JA, De Moor B. De Brabanter K, et al. Among authors: de brabanter j, de moor b. IEEE Trans Neural Netw. 2011 Jan;22(1):110-20. doi: 10.1109/TNN.2010.2087769. Epub 2010 Nov 1. IEEE Trans Neural Netw. 2011. PMID: 21047710

4

Low rank updated LS-SVM classifiers for fast variable selection.

Ojeda F, Suykens JA, De Moor B. Ojeda F, et al. Among authors: de moor b. Neural Netw. 2008 Mar-Apr;21(2-3):437-49. doi: 10.1016/j.neunet.2007.12.053. Epub 2008 Feb 2. Neural Netw. 2008. PMID: 18343309

5

Improved long-term temperature prediction by chaining of neural networks.

Duhoux M, Suykens J, De Moor B, Vandewalle J. Duhoux M, et al. Among authors: de moor b. Int J Neural Syst. 2001 Feb;11(1):1-10. doi: 10.1142/S012906570100045X. Int J Neural Syst. 2001. PMID: 11310550

6

Bayesian framework for least-squares support vector machine classifiers, gaussian processes, and kernel Fisher discriminant analysis.

Van Gestel T, Suykens JA, Lanckriet G, Lambrechts A, De Moor B, Vandewalle J. Van Gestel T, et al. Among authors: de moor b. Neural Comput. 2002 May;14(5):1115-47. doi: 10.1162/089976602753633411. Neural Comput. 2002. PMID: 11972910

7

Handling missing values in support vector machine classifiers.

Pelckmans K, De Brabanter J, Suykens JA, De Moor B. Pelckmans K, et al. Among authors: de brabanter j, de moor b. Neural Netw. 2005 Jun-Jul;18(5-6):684-92. doi: 10.1016/j.neunet.2005.06.025. Neural Netw. 2005. PMID: 16111866

8

Efficiently updating and tracking the dominant kernel principal components.

Hoegaerts L, De Lathauwer L, Goethals I, Suykens JA, Vandewalle J, De Moor B. Hoegaerts L, et al. Among authors: de lathauwer l, de moor b. Neural Netw. 2007 Mar;20(2):220-9. doi: 10.1016/j.neunet.2006.09.012. Epub 2007 Jan 17. Neural Netw. 2007. PMID: 17234385

9

A support vector machine formulation to PCA analysis and its kernel version.

Suykens JK, Van Gestel T, Vandewalle J, De Moor B. Suykens JK, et al. Among authors: de moor b. IEEE Trans Neural Netw. 2003;14(2):447-50. doi: 10.1109/TNN.2003.809414. IEEE Trans Neural Netw. 2003. PMID: 18238028

10

Financial time series prediction using least squares support vector machines within the evidence framework.

Van Gestel T, Suykens JK, Baestaens DE, Lambrechts A, Lanckriet G, Vandaele B, De Moor B, Vandewalle J. Van Gestel T, et al. Among authors: de moor b. IEEE Trans Neural Netw. 2001;12(4):809-21. doi: 10.1109/72.935093. IEEE Trans Neural Netw. 2001. PMID: 18249915

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